A Multicomponent mHealth-Based Intervention (SWAP IT) to Decrease the Consumption of Discretionary Foods Packed in School Lunchboxes: Type I ...
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JOURNAL OF MEDICAL INTERNET RESEARCH Sutherland et al
Original Paper
A Multicomponent mHealth-Based Intervention (SWAP IT) to
Decrease the Consumption of Discretionary Foods Packed in
School Lunchboxes: Type I Effectiveness–Implementation Hybrid
Cluster Randomized Controlled Trial
Rachel Sutherland1,2,3,4, PhD; Alison Brown1, BNutrDiet; Nicole Nathan1,3,4, PhD; Serene Yoong1,3,4,5, PhD; Lisa
Janssen1, BNutrDiet; Amelia Chooi1, BNutrDiet; Nayerra Hudson1, BNutrDiet; John Wiggers1,3,4, PhD; Nicola Kerr6,
BAppSc; Nicole Evans7, BPsych; Karen Gillham1, BSocSci; Christopher Oldmeadow3, PhD; Andrew Searles3, PhD;
Penny Reeves1,3, GradDipHlthEcon; Marc Davies8, BExSci; Kathryn Reilly1,3,4, PhD; Brad Cohen9, CA, BCOM-LLB;
Luke Wolfenden1, PhD
1
Hunter New England Population Health, Wallsend, Australia
2
School of Medicine and Public Health, University of Newcastle, Callaghan, Australia
3
Hunter Medical Research Institute, New Lambton Heights, Australia
4
Priority Research Centre for Health Behaviour, University of Newcastle, Callaghan, Australia
5
Department of Nursing and Allied Health, School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
6
Mid North Coast Local Health District, Port Macquarie, Australia
7
Central Coast Local Health District, Gosford, Australia
8
New South Wales Ministry of Health, Liverpool, Australia
9
SkoolBag, Sydney, Australia
Corresponding Author:
Rachel Sutherland, PhD
Hunter New England Population Health
Booth Building
Longworth Avenue
Wallsend
Australia
Phone: 61 2 4924 6499
Email: rachel.sutherland@health.nsw.gov.au
Abstract
Background: There is significant opportunity to improve the nutritional quality of foods packed in children’s school lunchboxes.
Interventions that are effective and scalable targeting the school and home environment are therefore warranted.
Objective: This study aimed to assess the effectiveness of a multicomponent, mobile health–based intervention, SWAP IT, in
reducing the energy contribution of discretionary (ie, less healthy) foods and drinks packed for children to consume at school.
Methods: A type I effectiveness–implementation hybrid cluster randomized controlled trial was conducted in 32 primary schools
located across 3 local health districts in New South Wales, Australia, to compare the effects of a 6-month intervention targeting
foods packed in children’s lunchboxes with those of a usual care control. Primary schools were eligible if they were not participating
in other nutrition studies and used the required school communication app. The Behaviour Change Wheel was used to co-design
the multicomponent SWAP IT intervention, which consisted of the following: school lunchbox nutrition guidelines, curriculum
lessons, information pushed to parents digitally via an existing school communication app, and additional parent resources to
address common barriers to packing healthy lunchboxes. The primary outcome, mean energy (kilojoules) content of discretionary
lunchbox foods and drinks packed in lunchboxes, was measured via observation using a validated school food checklist at baseline
(May 2019) and at 6-month follow-up (October 2019). Additional secondary outcomes included mean lunchbox energy from
discretionary foods consumed, mean total lunchbox energy packed and consumed, mean energy content of core lunchbox foods
packed and consumed, and percentage of lunchbox energy from discretionary and core foods, all of which were also measured
via observation using a validated school food checklist. Measures of school engagement, consumption of discretionary foods
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outside of school hours, and lunchbox cost were also collected at baseline and at 6-month follow-up. Data were analyzed via
hierarchical linear regression models, with controlling for clustering, socioeconomic status, and remoteness.
Results: A total of 3022 (3022/7212, 41.90%) students consented to participate in the evaluation (mean age 7.8 years; 1487/3022,
49.22% girls). There were significant reductions between the intervention and control groups in the primary trial outcome, mean
energy (kilojoules) content of discretionary foods packed in lunchboxes (–117.26 kJ; 95% CI –195.59 to –39.83; P=.003). Relative
to the control, the intervention also significantly reduced secondary outcomes regarding the mean total lunchbox energy (kilojoules)
packed (–88.38 kJ; 95% CI –172.84 to –3.92; P=.04) and consumed (–117.17 kJ; 95% CI –233.72 to –0.62; P=.05). There was
no significant difference between groups in measures of student engagement, consumption of discretionary foods outside of
school hours, or cost of foods packed in children’s lunchboxes.
Conclusions: The SWAP IT intervention was effective in reducing the energy content of foods packed for and consumed by
primary school–aged children at school. Dissemination of the SWAP IT program at a population level has the potential to influence
a significant proportion of primary school–aged children, impacting weight status and associated health care costs.
Trial Registration: Australian Clinical Trials Registry ACTRN12618001731280;
https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=376191&isReview=true
International Registered Report Identifier (IRRID): RR2-10.1186/s12889-019-7725-x
(J Med Internet Res 2021;23(6):e25256) doi: 10.2196/25256
KEYWORDS
childhood obesity; lunchboxes; children; child nutrition; mHealth; schools; hybrid; randomized controlled trial; technology
primary school aged children (mean age 7.96 years) found that
Introduction just 12% of students’ lunchboxes contain only core foods (ie,
Preventing the onset of overweight and obese status in children minimally processed foods recommended in Australian Dietary
is a global public health priority [1], given that it impacts Guidelines), with a quarter containing 4 or more discretionary
negatively on physical health, psychological well-being, and servings [16], exceeding the maximum daily amount for children
long-term chronic disease risk [2]. The frequent of this age. Similar nutrient compositions have been observed
overconsumption of energy-dense, nutrient-poor, or in lunchboxes across the globe, including in New Zealand [11],
“discretionary” foods throughout childhood, which displace the the United Kingdom [10,17], Canada [18], and the United States
consumption of core foods consistent with dietary guidelines, [19].
is known to be a major contributor to the development of being Current evidence regarding the effectiveness of school lunchbox
overweight and obese [3]. Of concern, the poor dietary patterns interventions is equivocal. A recent systematic review of such
that are established in childhood track into adulthood and interventions in the school and childcare setting identified just
increase the risk of adults being overweight or obese [4]. To 10 trials and suggested they had little to no effect on the
address this, the World Health Organization (WHO) nutritional quality of foods packed or consumed by students
recommends implementing populationwide interventions to [20]. Existing interventions have employed either passive
support the establishment of eating habits in children that are information dissemination strategies to parents, which have
consistent with dietary guidelines [5]. limited reach and engagement, or have used intensive
Children consume up to two-thirds of their daily energy intake face-to-face group-based strategies attracting a biased population
at school [6]. Consequently, schools have been identified as an group and presenting considerable challenges to implement at
optimal setting to implement public health nutrition interventions scale.
[5]. Internationally, school-based nutrition research has focused Mobile text messaging– and mobile app–based interventions
on improving the provision or sale of foods at school canteens have been proven to be a scalable and effective approach for
[7] or cafeterias [8]. However, in many countries, such as improving a variety of health behaviors—including those of
Australia [9], the United Kingdom [10], New Zealand [11], and parents—to provide a better child diet [21,22]. Our previous
Denmark [12], a significant proportion of children consume pilot study in 12 schools, assessing the feasibility, acceptability,
food brought to school from home in a lunchbox. Further, and potential efficacy of the multicomponent SWAP IT
research suggests that the nutritional quality of foods packed intervention [16], used an existing school mobile communication
in school lunchboxes may be poorer than that available at or app, along with newly developed school nutrition guidelines,
provided by schools. For example, in Australia, approximately school curriculum, and resources for parents, to encourage a
5% of items sold at school canteens are discretionary items [13] “swap” in their children’s lunchboxes of discretionary foods to
compared to 40% in children’s lunchboxes [14]. A healthier alternatives consistent with the Australian Dietary
cross-sectional study undertaken in Australia of 1681 students Guidelines (“everyday“ foods) [23]. The intervention approach
found that lunchboxes contain an average of 3.1 servings of was found to be highly feasible to deliver and acceptable to
discretionary foods (1200 kJ) and contributed to over 3000 kJ, both schools and parents, demonstrating promising short-term
which is significantly higher than that recommended in dietary improvements in the nutritional quality of foods packed in
guidelines [15]. A further Australian study involving 2143 lunchboxes [16]. Following the encouraging findings of the
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pilot study, our primary aim was to conduct an adequately University of Newcastle (reference #H-2008-0343) and the New
powered randomized trial to assess the effectiveness of the South Wales (NSW) State Education Research Applications
SWAP IT multicomponent lunchbox intervention in reducing Process (#2018247) and was prospectively registered with
the kilojoule content from discretionary foods and drinks both Australian New Zealand Clinical Trials Register
packed and consumed by children from school lunchboxes while (#12618001731280). A detailed description of the methods and
at school relative to usual care. We also sought to evaluate the intervention are outlined in the study protocol [25].
effectiveness of the intervention on a range of secondary
outcomes, including mean lunchbox energy from discretionary
Study Design and Setting
foods consumed, mean total lunchbox energy packed and A type I effectiveness–implementation hybrid cluster
consumed, mean energy content of core lunchbox foods packed randomized controlled trial was conducted with 32 primary
and consumed, percentage of lunchbox energy from (students aged approximately 5-12 years) schools across 3 local
discretionary and core foods, measures of school engagement, health districts in NSW, Australia (Figure 1). Schools were
consumption of discretionary foods outside of school hours, randomized to receive a 6-month (2 school terms),
and lunchbox cost. multicomponent lunchbox intervention or a usual care control
arm (16 schools per arm). Outcome assessments were conducted
Methods in a cohort of students at baseline and at 6 months after
randomization. The primary outcome was mean energy
Ethics and Registration (kilojoules) content of discretionary lunchbox foods and drinks
The research was conducted and reported in accordance with packed in lunchboxes assessed via lunchbox observation. Other
the requirements of the Consolidated Standards of Reporting registered outcomes related to implementation processes,
Trials (CONSORT) statement [24]. Approval to conduct this including intervention acceptability, appropriateness and
study was obtained from the Hunter New England Human feasibility [26,27], cost-effectiveness of the intervention, and
Research Ethics Committee (reference #06/07/26/4.04), impact on mean daily nutrient consumption, will be reported
separately.
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Figure 1. Consolidated Standards of Reporting Trials (CONSORT) flow diagram.
research studies. Schools purchase the communication app for
Sample and Participants a nominal fee annually, which is then free for parents to
Schools download, enabling direct school–parent communication. The
app is used by approximately 60% of schools in the region.
Schools were considered eligible if they met the following
Central schools (catering for students aged 5-18 years) and
criteria: government primary schools catering for students from
schools primarily catering for children with additional needs
kindergarten to year 6 and located in one of the participating
(such as intellectual disabilities) were excluded. According to
local health districts, greater than 120 student enrolments,
a random number generator in Excel (Microsoft Corporation),
current users of the preferred school mobile communication
eligible schools meeting the above criteria were sent a letter of
app (SkoolBag), and not participating in other nutrition-based
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invitation in random order. One week following the invitation, encompassing a review of published literature; focus groups
a member of the research team contacted the school principal with parents to identify local contextual barriers; telephone
via telephone to seek consent. A face-to-face meeting was interviews with parents (n=228) (L Janssen, R Sutherland, and
offered to all schools to outline the requirements of the study. N Nathan; unpublished data, 2019) and principals (n=196) [32]
Recruitment and consent of schools occurred between February to assess barriers, acceptability of intervention strategies, and
2019 and May 2019. Recruitment continued until 32 schools content and delivery mode; and a literature review of existing
provided active signed principal consent to participate. lunchbox interventions [20] were undertaken to select behavior
change techniques and strategies to support parents to pack
Parents and Students healthy school lunchboxes. Multimedia Appendix 1 outlines
Opt-in parental consent was required for children and parents the Behaviour Change Wheel mapping process and outlines the
to participate in the evaluation of the behavioral outcomes. chosen behavior change techniques incorporated into the SWAP
Parents were also required to be active users of the school IT intervention.
communication app, defined as downloading the school
communication app on the parent consent form. A strategy to Figure 2 provides an overview of the SWAP IT intervention
recruit parents and students was developed based on the pilot logic. The SWAP IT intervention encouraged lunchbox “swaps”
study and reviews of evidence for facilitating participation in from discretionary food items to Australian Dietary
school-based research [16,28]. Following principal consent, all Guideline–based healthier alternatives known as “everyday”
parents with a child enrolled in classes from kindergarten to foods. The multicomponent lunchbox intervention consisted of
year 6 (5-12 years) were invited to participate in the study 4 strategies outlined in the following section. The mobile health
evaluation measures, which included a lunchbox observational component included weekly pushed messages to parents
assessment, parent survey, and student survey (year 5 and 6 delivered via an existing school mobile communication app,
students). Students were provided with an information package SkoolBag, in addition to embedding lunchbox content within
outlining the study and a consent form. Parents were asked via the app for parents to access. A detailed 4-part description of
the consent form if they were an active user (ie, downloaded the intervention has been published in a protocol [25], and the
the app) of the school communication app. One week after the intervention included the 4 following strategies:
information package was distributed, parents who had not 1. Lunchbox nutrition guidelines: Using a template developed
returned a consent form were telephoned by school-employed by the project team, school principals developed, endorsed,
staff. A replacement consent form was distributed via mail to and disseminated nutrition guidelines to parents which were
parents who provided verbal consent over the phone. consistent with the WHO and the NSW Department of
Education Nutrition in Schools policy [33]. Guidelines were
Randomization and Blinding
disseminated to parents in the first 5 weeks of the
Following baseline data collection, schools (cluster) were intervention via the SkoolBag app and school newsletters
randomly allocated in a 1:1 ratio to the intervention or control to demonstrate schools’ endorsement of the SWAP-IT
group based on a random number function in Excel. program.
Randomization was undertaken by a statistician not involved 2. Weekly pushed lunchbox messages: Through the SkoolBag
in contacting schools in the study intervention or assessment app, 10 weekly electronic messages (push notifications) to
and stratified by the socioeconomic status of school locality support the packing of healthy lunchboxes were
using the Socio-Economic Indexes for Areas (SEIFA 2016), as disseminated to parents or caregivers. Messages were
socioeconomic status is associated with lunchbox contents and codeveloped by the research team, public health
child diet [29,30]. Research personnel involved in data collection nutritionists, health promotion practitioners, teachers, and
and lunchbox content analysis were blind to group allocation, parents and were optimized and refined via a study
as all identifiable school information was removed prior to data involving 511 parents [34]. The distribution of the messages
analysis. Data collection staff were not informed of group via the school communication app was managed centrally
allocation; however, this might have been disclosed to them by by the project team. This allowed all parents at the schools
school staff during field activity. Due to the inability to conceal allocated to the intervention group who had downloaded
intervention delivery, school personnel were notified of their the app to receive the pushed messages via the research
group allocation via a phone call. team and prevented the need to rely on each individual
Multicomponent Intervention school to push the weekly content to parents. This centrally
coordinated effort therefore did not require school time or
The multicomponent intervention based on the previous pilot
resources and thereby maximized the fidelity of the
was codeveloped by a multidisciplinary team comprising
intervention. The pushed messages aligned to
academic and end-user stakeholders from government health
parent-reported barriers to packing healthy school
agencies, educational systems, universities, and technology
lunchboxes: lack of time or convenience, knowledge of
partners and included parent representatives with expertise in
suitable swaps, child preference, cost, food safety, and lack
nutrition, school-based health interventions, behavior change,
of school nutrition policy. Where possible, a swap within
implementation science, and technology-based interventions.
the same food category was suggested (eg, for packaged
Conceptual Framework foods). The pushed messages were designed to act as
The Behaviour Change Wheel [31] was used to guide the prompts and cues to reinforce packing of everyday foods.
development of the intervention. Extensive formative research The messages were connected to embedded videos
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developed by the research team to align to parent-reported 4. Curriculum resources for schools: Schools were provided
barriers which provided tips and suggestions to assist with a short online teacher professional learning module
parents to pack everyday foods that were quick, convenient, (10 minutes) developed by the research team, which
and low cost and to connect parents with tools and resources included public health nutritionists, health promotion
to improve their knowledge and skills to swap out practitioners, and teachers outlining the rationale for the
discretionary foods and pack everyday foods. study and providing the skills and resources required to
3. Resources for parents: Links embedded in the app messages deliver the classroom curriculum lessons. Schools were
connected parents with electronic resources housed on the also provided stage-appropriate curriculum resources which
program website. These resources provided information were codeveloped by the research team with input from
regarding health consequences, simple healthy lunchbox teachers, parents, and education partners to align with
swaps that addressed child preference, cost, convenience, syllabus outcomes that were developed by dietitians and
and food safety. Physical resources, including a SWAP IT teachers in order to reinforce healthy food preferences. This
ideas booklet (lunchbox ideas), clear drink bottle for water, required teachers to deliver 3 curriculum lessons 10 minutes
and an ice brick to support food safety, were also provided in duration. Curriculum resources were designed to address
to parents and were distributed to students and parents via the identified barrier to packing a healthy lunchbox of “child
the schools’ usual methods of dissemination. preference for discretionary foods.”
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Figure 2. SWAP IT logic model. BCT: behaviour change technique; HPS: health promoting schools framework.
group, and they participated in data collection only and
Control Schools continued usual school business.
Schools allocated to the control group had access to the
SkoolBag app but not the lunchbox intervention content. The Data Collection and Measures
SWAP IT website was freely accessible by the general public, Lunchbox Energy
including parents and schools; however, schools and parents
were not notified or directed to this site. There was no The primary outcome was the mean energy (kilojoules) content
information (nutrition or otherwise) provided to the control of discretionary foods packed in the school lunchboxes by
parents who were users of the school mobile app, assessed at
baseline and at 6-month follow-up. A detailed description of
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the study measures and data collection methods have been Procedures
described in a published protocol [25]. Lunchbox energy content To assess the foods packed in the lunchbox (premeal assessment)
was assessed from photos of lunchboxes taken at school by on a randomly selected school day prior to recess, at lunch, or
trained research assistants prior to the first meal break with a during in-class vegetable and fruit breaks [38,39], consenting
valid and reliable lunchbox observational audit, known as the students were asked to display the contents of their lunchbox
School Food Checklist (SFC) [35,36]. The SFC is a previously on their desk in the classroom. Parents and students were not
validated tool shown to be accurate and reliable in measuring informed of the exact day of data collection. A preprepared
energy from food and drinks for the Australian context. The paper grid was placed under the lunchbox contents and used to
SFC [35,36] enabled the assessment of the kilojoule content assess the scale and serving size of the items. Any foods not
and serving size for each lunchbox item. Two trained dietitians easily identified were discussed with the student and further
observed each school lunchbox photo and classified each food details were recorded on the grid paper prior to being
and drink item according to its SFC category as “everyday photographed. The photo was taken by trained research
foods” or “discretionary foods” and assessed the kilojoule assistants prior to any foods being consumed. Students were
content and serving size for each lunchbox item and the serving asked if they intended to purchase food from the canteen that
size. The checklist included 20 food and drink categories, day, and if so, were removed from the analysis.
including main food items, such as bread, fast food, and
leftovers/mixed dishes; and snack items such as noodles, To assess the consumption of foods packed in the lunchbox
packaged snacks, biscuits and crackers, chocolate and candy, (postmeal assessment), on the same day, students were asked
cheese, eggs, dried fruit and nuts, muesli and fruit bars, cakes to keep all unconsumed or partially consumed food items in
and buns, muffins and scones, pastries, desserts, yoghurt, fruit, their lunchboxes. Following all meal breaks, students were
vegetables, milk, soft drink, and water and fruit juice. asked to place unconsumed or partially consumed items from
“Everyday” items referred to food and drink items that were their lunchbox onto the grid paper, and a second photograph of
part of the core food groups as determined by the Australian all remaining food was taken. Measures relating to consumption
Dietary Guidelines [23]. Food items classified as “discretionary” were based on the second photograph of the day being taken
were items considered to be energy dense with minimal after all meal breaks had occurred and all uneaten food had been
nutritional value, including cakes, chocolate, candy, chips, placed back into the lunchbox container. Consumption was
muesli bars, and fast food [23]. The serving size of each calculated by subtracting the postmeal assessment from the
lunchbox item and kilojoules per serving information was premeal assessment.
obtained from FoodWorks Professional Edition V7 (version 7, Trained dietitians, blinded to group allocation, observed each
Xyris Software). To further aid this process, decision rules school lunchbox photo in order to classify each food and drink
developed in the previous study [16] were used to ensure item according to its SFC category and the serving size. All
standardization of assessments. lunchbox photos were assessed by 2 dietitians working together
The secondary outcomes associated with lunchbox energy were to make a consensus decision on the analysis for each lunchbox.
mean total energy (kilojoule) packed within the lunchbox; mean To further aid this process, decision rules were developed to
total energy (kilojoules) consumed from the lunchbox; mean ensure standardization of assessments. Differences in opinion
energy (kilojoules) from discretionary foods and drinks between dietitians were resolved following consultation with a
consumed within the lunchbox; mean energy (kilojoules) from third dietitian assessor. Following the analysis of the premeal
healthy foods packed and consumed from the lunchbox; and lunchbox photo, dietitians then analyzed the postmeal photo.
percentage of lunchbox energy from discretionary and healthy Energy consumption was calculated by subtracting the energy
foods and drinks, both packed and consumed. Data were content of foods and drinks remaining in students’ lunchboxes
collected at baseline and immediately after the 6-month at the postmeal assessment from the energy content of foods
intervention with the SFC as outlined in the previous section. and drinks in the lunchbox during premeal assessment (“foods
Following the analysis of the premeal lunchbox photo, dietitians consumed”).
analyzed the postmeal photo.
Student School Engagement
Student Consumption of Discretionary Foods Outside We also assessed impact on engagement, as research suggests
of School Hours that improved nutrition correlates with greater school attendance,
At baseline and at follow-up, parents were asked to report, via improved concentration, and higher academic achievement [40].
a short telephone survey, on their child’s intake of discretionary At baseline and at follow-up, students in years 5 and 6
foods outside of school hours and on weekends to identify any completed selected items from the validated School Engagement
compensatory nutrition behavior occurring outside of school Measure via a pen and paper survey. The School Engagement
hours. Measures were taken from the NSW Schools Physical Measure is a 19-item survey that provides a measure of students’
Activity and Nutrition Survey [37]. Parents reported on the behavioural (5 items), emotional (6 items), and cognitive
following 6 categories of discretionary foods: (1) fried potato engagement (8 items) at school, which are outcomes considered
products, (2) potato chips and other salty snacks, (3) sweet important for achieving positive academic outcomes [41].
biscuits and cakes, (4) confectionary, (5) ice cream or ice blocks,
and (6) fruit juice. The frequency of consumption for consenting
students was reported at baseline and immediately after the
intervention at 6 months.
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Student Consumption of Discretionary Foods Outside for SEIFA, remoteness, and baseline values, and a random level
of School Hours intercept for schools was included to adjust for the clustered
design of the study. Analysis followed intention-to-treat
To ensure any reduction in energy intake occurring while at
principles, where schools and students were analyzed according
school did not result in compensatory intake outside of school
to their randomized treatment allocation. All statistical tests
hours (potential adverse event), parents were asked via a short
were 2-tailed with an α of .05. As specified in the study protocol
telephone survey at baseline and at follow-up to report on their
[25], data were analyzed only for students whose parents had
eldest eligible child’s intake of discretionary foods outside of
reported downloading the required SkoolBag app to ensure
school hours and on weekends. Measures were taken from the
exposure to the intervention, and students intending to purchase
NSW Schools Physical Activity and Nutrition Survey [37].
food or drinks from the canteen or who did not bring lunch were
Parents reported on 6 categories of discretionary foods, including
removed from the primary analysis to focus on students whose
(1) fried potato products, (2) potato chips and other salty snacks,
lunchbox was their source of energy for the day [16].
(3) sweet biscuits and cakes, (4) confectionary, (5) ice cream
or ice blocks, and (6) fruit juice and reported the frequency of Sample Size and Power
consumption as never or rarely, 1 to 2 times per week, 3 to 4 According to our pilot results [16], a standard lunchbox contains
times per week, 5 to 6 times per week, once per day, or 2 or 1089 kJ (SD 900 kJ) of discretionary foods. With an intraclass
more times per day. correlation coefficient of 0.05, 32 schools with 140 students per
Lunchbox Cost school enabled detection of a 200-kJ difference between groups
at follow-up on the primary trial outcome, with 80% power at
It has been hypothesized that one potential adverse effect of
a significance level of PJOURNAL OF MEDICAL INTERNET RESEARCH Sutherland et al
Table 1. Sample characteristics of schools and students at baseline.
Characteristics Intervention Control
Schools
Allocation, n 16 16
Location, n (%)
Urban 8 (50.0) 6 (37.5)
Rural 8 (50.0) 10 (62.5)
Socioeconomic statusa, n (%)
Most disadvantaged 13 (81.2) 13 (81.2)
Least disadvantaged 3 (18.8) 3 (18.8)
Schools with greater than 10% Aboriginal or Torres Strait Islander student enrolments, n 10 11
Students
Allocation, n 1216 1176
Sex, n (%)b
Female 592 (50.04) 550 (48.37)
Male 591 (49.96) 587 (51.63)
Mean age (years) 7.88 7.68
Socioeconomic statusa, n (%)
Most disadvantaged 938 (77.14) 789 (67.09)
Least disadvantaged 278 (22.86) 387 (32.91)
a
Socioeconomic status is based on SEIFA Index of relative socioeconomic disadvantage 2016: most disadvantaged = lowest quartiles of SEIFA; least
disadvantaged = highest quartiles of SEIFA.
b
Information on sex missing for 72 students.
intervention group. There was also a significant reduction in
Primary Outcome: Mean Energy (Kilojoules) Content percentage of lunchbox energy packed from discretionary foods
of Discretionary Foods Packed From the School between groups (–3.16%; 95% CI –5.46 to –0.86; P=.01), while
Lunchboxes the percentage of lunchbox energy from everyday foods
At 6-month follow-up, the difference between the intervention increased (3.16%; 95% CI 0.86-5.46; P=.01). A significant
and control group in the mean energy (kilojoules) content of reduction favoring the intervention group in the mean energy
discretionary foods packed in school lunchboxes was –117.71 (kilojoules) from discretionary foods consumed from lunchboxes
kJ (95% CI –195.59 to –39.83; P=.003). A sensitivity analysis (–96.31kJ; 95% CI –194.63 to 2.01; P=.05) was also observed.
on the primary outcome using complete cases indicated a similar There was no statistical difference between groups in the mean
result of –120.43kJ (95% CI –200.82 to –40.04; P=.005). lunchbox energy from everyday foods (kilojoules) packed in
lunchboxes (32.85 kJ; 95% CI –31.61 to 97.31; P=.31) or
Secondary Lunchbox Energy Outcomes consumed (–21.91 kJ; 95% CI –112.38 to 68.56; P=.62). Table
The mean total energy (kilojoules) packed in lunchboxes (–88.38 2 outlines the lunchbox energy packed and consumed by group.
kJ; 95% CI –172.84 to –3.92; P=.04) and mean total energy Multimedia Appendix 2 outlines the food and drink items packed
(kilojoules) consumed from lunchboxes (–117.17kJ; 95% CI in lunchboxes.
–233.72 to –0.62; P=.05) both reduced in favor of the
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Table 2. Mean energy and percentage of energy from everyday and discretionary foods packed and consumed from student lunchboxes.
Outcome Intervention Control Difference in energy be- P value
tween groups at follow-up,
mean (95% CI)
Baseline mean, Follow-up, mean Baseline, mean Follow-up, mean
(SD) (n=1216) (SD) (n=946) (SD) (n=1176) (SD) (n=886)
Daily energy (kilojoules) packed in student lunchboxes
Primary outcome: lunch- 1214.86 (876.49) 1156.77 (841.76) 1067.38 (898.82) 1105.06 (859.06) –117.26 (–195.59 to 39.83) .003
box energy from discre-
tionary foods packed in
lunchboxes
Lunchbox energy from ev- 1616.19 (628.34) 1610.93 (624.41) 1644.17 (621.73) 1605.81 (610.02) 32.85 (–31.61 to 97.31) .31
eryday foods packed in
lunchboxes
Total lunchbox energy 2831.05 (927.81) 2767.70 (873.52) 2711.54 (962.33) 2710.87 (878.44) –88.38 (–172.84 to –3.92) .04
packed in lunchboxes
Daily lunchbox energy (kilojoules) consumed by students
Lunchbox energy from 901.30 (745.60) 876.70 (717.23) 744.19 (717.20) 802.75 (677.23) –96.31 (–194.63 to 2.01) .05
discretionary foods con-
sumed from lunchboxes
Lunchbox energy from ev- 1270.85 (631.79) 1282.56 (622.95) 1304.69 (600.58) 1341.72 (607.53) –21.91 (–112.38 to 68.56) .62
eryday foods consumed
from lunchboxes
Total lunchbox energy 2172.15 (895.82) 2159.26 (810.78) 2048.88 (853.84) 2144.48 (743.22) –117.17 (–233.72 to –0.62) .05
consumed from lunchbox-
es
Lunchbox energy coming from discretionary and everyday foods (%)
Packed lunchbox energy 40.10 (23.31) 39.04 (23.94) 35.84 (23.69) 37.90 (23.81) –3.16 (–5.46 to –0.86) .01
from discretionary foods
Packed lunchbox energy 59.90 (23.31) 60.96 (23.94) 64.16 (23.69) 62.10 (23.81) 3.16 (0.86 to 5.46) .01
from everyday foods
Total cost (Aus $) of lunch- 3.94 (1.35) 3.91 (1.36) 3.78 (1.38) 3.78 (1.32) –0.06 (–0.18 to 0.07) .37
box items
for student total school engagement measure score (–0.08; 95%
Student Engagement CI –0.18 to 0.02; P=.10), student behaviour (–0.05; 95% CI
Table 3 outlines the student engagement measures. There were –0.15 to 0.04; P=.24), or emotional (–0.08; 95% CI –0.2 to 0.06;
no observed differences between groups for any measure of P=.26) or cognitive engagement (0.09; 95% CI –0.22 to 0.05;
student engagement after the 6-month intervention, including P=.20).
Table 3. Mean school engagement measure by group at baseline and at follow-up.
Mean school engagement Intervention Control Difference in engagement P value
score between groups at follow-
up, mean (95% CI)
Baseline, mean Follow-up, mean Baseline, mean Follow-up, mean
(SD) (n=364) (SD) (n=309) (SD) (n=299) (SD) (n=241)
Behavior score 4.12 (0.59) 4.09 (0.62) 4.11 (0.65) 4.14 (0.66) –0.05 (–0.15 to 0.04) .24
Emotion score 3.55 (0.91) 3.33 (0.99) 3.56 (0.92) 3.40 (0.98) –0.08 (–0.22 to 0.06) .26
Cognitive score 2.92 (0.87) 2.80 (0.87) 2.87 (0.83) 2.83 (0.85) –0.09 (–0.22 to 0.05) .20
Total school engagement 3.44 (0.66) 3.31 (0.71) 3.42 (0.68) 3.35 (0.70) –0.08 (–0.18 to 0.02) .10
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Student Consumption of Discretionary Foods Outside change, our research team conducted extensive formative
of School Hours assessment including mapping barriers and consulting with key
stakeholders to co-design the SWAP IT using a theoretical
There were no differences between groups in the foods
framework. The multicomponent program also dually targeted
consumed outside of school hours, indicating no compensatory
both the school, via guidelines and curriculum, and parents, via
consumption of discretionary foods outside of care.
direct messages, which may explain the favorable intervention
Lunchbox Cost effect.
The total cost of lunchbox foods following the intervention did To improve health at a population level, interventions shown
not differ between groups (–Aus $0.06; 95% CI –0.18 to 0.07; to be effective under research conditions need to be scaled up
P=.37; Table 2). to reach a large proportion of the population [46]. Few
school-based behavioral interventions have been suggested to
Discussion be suitable for large-scale dissemination, as they require
expertise and resources not readily available within schools and
This trial investigated the effectiveness of the SWAP IT often use high-intensity delivery modes, such as face-to-face
intervention on the energy of students’ lunchbox foods, both training [20]. The use of digital delivery modes overcome many
packed and consumed, using an existing school communication of these barriers; however, poor adoption and ongoing
app provided directly to parents. Relative to lunchboxes in the engagement with new apps or websites, for example, are often
control group, the lunchboxes in the intervention group an impediment to population-level reach and improved health
contained significantly less mean energy from discretionary gains [47]. To address this, the SWAP IT intervention adopted
foods corresponding to 117 kJ per day or a 600 kJ reduction a multicomponent intervention design that addressed many of
over a school week. The SWAP IT intervention also resulted the existing limitations [20]. Incorporating the SWAP IT
in a reduction in mean energy from discretionary foods that behavioral intervention components as a complement into an
were consumed by students (96.31 kJ). The mean total lunchbox existing school communication app that had already been
energy both packed and consumed was also significantly less adopted by schools and downloaded by parents was undertaken
in intervention lunchboxes, and the percentage of energy from to overcome the challenges of both population-level reach and
discretionary foods decreased by 3.16%, while percentage digital engagement. A similar approach, in which a nutrition
energy from everyday foods correspondingly increased. The intervention was embedded into an existing online school
lunchbox energy coming from everyday foods that were canteen ordering system, also resulted in a significant
consistent with dietary guidelines did not statistically differ intervention effect on energy, sugar, and fat [48]. This suggests
between groups, indicating the change in total energy observed that embedding digital interventions within existing systems
was primarily from a reduction in discretionary foods. These supported by additional behavior change strategies may be
favorable nutrition outcomes occurred while the cost of packing superior to developing and implementing new digital health
a lunchbox remained stable across groups, indicating the changes interventions alone.
made to lunchboxes did not result in additional costs. The
intervention, however, did not result in changes to student school Although a reduction in energy from discretionary foods of 600
engagement at school. kJ per week may appear small at an individual level, at a
population level, it has the potential to lower the risk of
Although it is challenging to make direct comparisons, the individuals being overweight or obese, result in a gain of
magnitude of reduction in energy from discretionary foods health-adjusted life years, and make a significant contribution
appears favorable compared to previous lunchbox interventions. toward savings in health care costs [49]. Given its potential
Of the 10 included studies within a systematic review of reach, with 86% of students taking a packed lunch on a daily
lunchbox interventions conducted within the school and basis [9] and 90% of those students packing at least 1 serving
childcare environment [20], 4 targeted the packing of of discretionary food in their lunchbox [14], this intervention
discretionary foods, with evidence for the effectiveness of has the potential to immensely shift the consumption of
interventions on what is packed in lunchboxes in relation to discretionary foods. Further, as 60% to 70% [50] of schools in
discretionary foods, sugar-sweetened drinks, or other core foods NSW Australia and the United States, respectively, already
being equivocal. Of the 2 studies conducted in the school using school communication apps, interventions such as SWAP
environment, results were mixed, with interventions impacting IT have the potential to reach millions of parents on a daily
on the reduction of either high fat salty snacks (reduction of 2.8 basis. Further investigation evaluating the cost-effectiveness
gm of savory snacks; P=.04) or sweet confectionary, fruit drinks, and implementation process of the SWAP IT intervention is
or candy (–0.43 servings; P=.001), but not both [20]. needed to confirm if the SWAP IT intervention warrants large
Furthermore, only 1 study within this review used a similar scale dissemination. Future research should focus on developing
methodology of assessment based on lunchbox photography strategies that maximize the adoption or uptake of the SWAP
and observation to estimate serving size [20]. The study found IT intervention by schools at scale and methods for sustaining
no significant effect on the nutritional quality of food brought school engagement to continue the impact on parent behavior
from home, which might have been due to a lack of power or change.
a result of the complex information dissemination pathway of
their intervention that involved sending messages to parents via The results of this trial should be interpreted within the context
newsletters and lessons delivered to children. To target behavior of its strengths and limitations. Study strengths include the
experimental hybrid design, with randomized controlled trials
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being considered the gold standard for evaluating causal effects Although the large-scale dissemination of pushed messages to
of interventions. The SWAP IT trial was also developed using parents via the app is feasible and has high fidelity,
behavior change theory and used direct observation and implementation of school-level strategies may require additional
validated tools to assess lunchbox contents, which strengthened support. This trial had a follow-up period of 6 months, and the
the ability of the study to accurately measure the true impact of long-term sustainability of the intervention in both schools and
the study outcomes. Although the effect size of the SWAP IT with parents is unknown. Further investigation is warranted to
effectiveness trial was smaller than that of the previous pilot ensure the intervention has an ongoing desirable impact on
[16], the significant results were replicated, indicating that, lunchbox behavior.
pending further evaluation exploring the implementation
The SWAP IT intervention presents an effective digital behavior
outcomes and cost effectiveness, the intervention warrants
change solution to a large and long-standing public health
consideration for large-scale dissemination. However, a number
problem of a high consumption of discretionary foods by
of limitations should be considered. The trial had a lower than
children while at school. Given the significant impact on
anticipated participation and consent rate from schools and
lunchbox food energy that has been demonstrated by the
particularly parents, with only 41.90% of parents consenting to
previous pilot trial and replicated in this effectiveness trial at a
participate in the lunchbox observations. Upon enquiry, we
larger scale, the intervention provides an attractive option to
believe this is primarily due to the measurement component, in
policy makers to complement existing public health programs
which lunchbox observations might have been considered an
targeting the school nutrition environment. Following further
encroachment on privacy [51], given that the acceptability of
evaluation to determine its implementation process, outcomes,
the intervention for schools and parents was high at 84% [16].
and cost-effectiveness, models to further scale up and maximize
The intervention was also multicomponent, and isolating the
the adoption of SWAP IT will ensure that a public health benefit
impact of each individual strategy was not possible in this trial.
can be realized.
Acknowledgments
The authors wish to acknowledge the research assistants involved and the schools participating in this study, as well as the
Department of Education, Hunter Region, for their contribution to the advisory group.
This project was funded by the NSW Ministry of Health, Translational Research Grant Scheme. The NSW Ministry of Health
has not had any role in the design, data collection, analysis of data, interpretation of data, or dissemination of the study. The
project also received infrastructure support from the Hunter Medical Research Institute. RS is supported by a National Health
and Medical Research Council Translating Research into Practice Fellowship (#APP1150661) and a Hunter New England Clinical
Research Fellowship; LW is supported by a National Health and Medical Research Council Career Development Fellowship
(#APP1128348) and a Heart Foundation Future Leader Fellowship; SY is supported by a Discovery Early Career Researcher
Award fellowship.
Authors' Contributions
RS and AB led the development of this manuscript. RS, NN, SY, LJ, and LW conceived the intervention concept. RS and LW
secured funding for the study. RS, NN, and LW guided the design and piloting of the intervention. RS, AB, LW, NN, LJ, and
JW guided the evaluation design and data collection. CO developed the analysis plan. RS, AB, NN, LJ, JW, NK, NE, CO, AS,
PR, CR, BS, MD, KR, BC, KG, and LW are all members of the advisory group that oversee the program and monitor data. RR,
AW, NH, AB, LJ, and AC are all members of the project team that oversee the implementation and evaluation of the program.
All authors contributed to developing the protocols and reviewing, editing, and approving the final version of the paper.
Conflicts of Interest
Authors RS, NN, LW, KG, NE, and JW receive salary support from their respective local health districts. Hunter New England
Local Health District contributes funding to the project outlined in this protocol. None of these agencies were involved in the
peer review of this grant. RS and NN are associate editors for BMC Public Health. All other authors declare that they have no
competing interests.
Multimedia Appendix 1
Using the Behaviour Change Wheel process to map barriers to packing healthy lunchboxes with identified intervention functions
and suitable behaviour change techniques (BCTs).
[DOCX File , 16 KB-Multimedia Appendix 1]
Multimedia Appendix 2
Food and drink items packed in lunchboxes.
[DOCX File , 15 KB-Multimedia Appendix 2]
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Multimedia Appendix 3
CONSORT-eHEALTH checklist (V 1.6.1).
[PDF File (Adobe PDF File), 1164 KB-Multimedia Appendix 3]
References
1. Report of the Commission on Ending Childhood Obesity: implementation plan: executive summary. World Health
Organization. 2017. URL: https://www.who.int/end-childhood-obesity/publications/echo-plan-executive-summary/en/
[accessed 2021-05-01]
2. Lobstein T, Baur L, Uauy R. Obesity in children and young people: a crisis in public health. Obes Rev 2004 May;5 Suppl
1:4-104. [doi: 10.1111/j.1467-789X.2004.00133.x] [Medline: 15096099]
3. Chung A, Peeters A, Gearon E, Backholer K. Contribution of discretionary food and drink consumption to socio-economic
inequalities in children's weight: prospective study of Australian children. Int J Epidemiol 2018 Jun 01;47(3):820-828. [doi:
10.1093/ije/dyy020] [Medline: 29514246]
4. Venn AJ, Thomson RJ, Schmidt MD, Cleland VJ, Curry BA, Gennat HC, et al. Overweight and obesity from childhood to
adulthood: a follow-up of participants in the 1985 Australian Schools Health and Fitness Survey. Med J Aust 2007 May
07;186(9):458-460. [doi: 10.5694/j.1326-5377.2007.tb00997.x] [Medline: 17484707]
5. School policy framework: implementation of the WHO global strategy on diet, physical activity and health. World Health
Organization. 2008. URL: https://apps.who.int/iris/handle/10665/43923 [accessed 2021-05-01]
6. Sharma M. School-based interventions for childhood and adolescent obesity. Obes Rev 2006 Aug;7(3):261-269. [doi:
10.1111/j.1467-789X.2006.00227.x] [Medline: 16866974]
7. Wolfenden L, Nathan N, Williams CM, Delaney T, Reilly KL, Freund M, et al. A randomised controlled trial of an
intervention to increase the implementation of a healthy canteen policy in Australian primary schools: study protocol.
Implement Sci 2014 Oct 11;9:147. [doi: 10.1186/s13012-014-0147-3] [Medline: 25300221]
8. Kessler HS. Simple interventions to improve healthy eating behaviors in the school cafeteria. Nutr Rev 2016
Mar;74(3):198-209 [FREE Full text] [doi: 10.1093/nutrit/nuv109] [Medline: 26874753]
9. Bell AC, Swinburn BA. What are the key food groups to target for preventing obesity and improving nutrition in schools?
Eur J Clin Nutr 2004 Feb;58(2):258-263 [FREE Full text] [doi: 10.1038/sj.ejcn.1601775] [Medline: 14749745]
10. Evans CEL, Greenwood DC, Thomas JD, Cade JE. A cross-sectional survey of children's packed lunches in the UK: food-
and nutrient-based results. J Epidemiol Community Health 2010 Nov;64(11):977-983. [doi: 10.1136/jech.2008.085977]
[Medline: 20089755]
11. Dresler-Hawke E, Whitehead D, Coad J. What are New Zealand children eating at school? A content analysis of `consumed
versus unconsumed' food groups in a lunch-box survey. Health Education Journal 2009 Mar;68(1):3-13. [doi:
10.1177/0017896908100444]
12. Sabinsky MS, Toft U, Sommer HM, Tetens I. Effect of implementing school meals compared with packed lunches on
quality of dietary intake among children aged 7-13 years. J Nutr Sci 2019;8:e3 [FREE Full text] [doi: 10.1017/jns.2018.29]
[Medline: 30746124]
13. Clinton-McHarg T, Janssen L, Delaney T, Reilly K, Regan T, Nathan N, et al. Availability of food and beverage items on
school canteen menus and association with items purchased by children of primary-school age. Public Health Nutr 2018
Oct;21(15):2907-2914. [doi: 10.1017/S1368980018001726] [Medline: 30070193]
14. Sutherland R, Nathan N, Brown A, Yoong S, Reynolds R, Walton A, et al. A cross-sectional study to determine the energy
density and nutritional quality of primary-school children's lunchboxes. Public Health Nutr 2020 Apr;23(6):1108-1116.
[doi: 10.1017/S1368980019003379] [Medline: 31969199]
15. Sanigorski AM, Bell AC, Kremer PJ, Swinburn BA. Lunchbox contents of Australian school children: room for improvement.
Eur J Clin Nutr 2005 Nov;59(11):1310-1316 [FREE Full text] [doi: 10.1038/sj.ejcn.1602244] [Medline: 16034359]
16. Sutherland R, Nathan N, Brown A, Yoong S, Finch M, Lecathelinais C, et al. A randomized controlled trial to assess the
potential efficacy, feasibility and acceptability of an m-health intervention targeting parents of school aged children to
improve the nutritional quality of foods packed in the lunchbox 'SWAP IT'. Int J Behav Nutr Phys Act 2019 Jul 02;16(1):54
[FREE Full text] [doi: 10.1186/s12966-019-0812-7] [Medline: 31266506]
17. Evans CEL, Melia KE, Rippin HL, Hancock N, Cade J. A repeated cross-sectional survey assessing changes in diet and
nutrient quality of English primary school children's packed lunches between 2006 and 2016. BMJ Open 2020 Jan
13;10(1):e029688 [FREE Full text] [doi: 10.1136/bmjopen-2019-029688] [Medline: 31932386]
18. Everitt T, Engler-Stringer R, Martin W, Vatanparast H. Comparing diet quality of school meals versus food brought from
home. Can J Diet Pract Res 2020 Jun 04:1-7. [doi: 10.3148/cjdpr-2020-013] [Medline: 32495636]
19. Hur I, Burgess-Champoux T, Reicks M. Higher quality intake from school lunch meals compared with bagged lunches.
ICAN: Infant, Child, & Adolescent Nutrition 2011 Mar 29;3(2):70-75. [doi: 10.1177/1941406411399124]
20. Nathan N, Janssen L, Sutherland R, Hodder RK, Evans CEL, Booth D, et al. The effectiveness of lunchbox interventions
on improving the foods and beverages packed and consumed by children at centre-based care or school: a systematic review
https://www.jmir.org/2021/6/e25256/ J Med Internet Res 2021 | vol. 23 | iss. 6 | e25256 | p. 14
(page number not for citation purposes)
XSL• FO
RenderXJOURNAL OF MEDICAL INTERNET RESEARCH Sutherland et al
and meta-analysis. Int J Behav Nutr Phys Act 2019 Apr 29;16(1):38 [FREE Full text] [doi: 10.1186/s12966-019-0798-1]
[Medline: 31036038]
21. Hammersley ML, Jones RA, Okely AD. Parent-focused childhood and adolescent overweight and obesity eHealth
interventions: a systematic review and meta-analysis. J Med Internet Res 2016 Jul 21;18(7):e203 [FREE Full text] [doi:
10.2196/jmir.5893] [Medline: 27443862]
22. Zarnowiecki D, Mauch CE, Middleton G, Matwiejczyk L, Watson WL, Dibbs J, et al. A systematic evaluation of digital
nutrition promotion websites and apps for supporting parents to influence children's nutrition. Int J Behav Nutr Phys Act
2020 Feb 10;17(1):17 [FREE Full text] [doi: 10.1186/s12966-020-0915-1] [Medline: 32041640]
23. Australian Dietary Guidelines. National Health Medical Research Council. 2013. URL: https://www.nhmrc.gov.au/adg
[accessed 2021-05-01]
24. Eldridge SM, Chan CL, Campbell MJ, Bond CM, Hopewell S, Thabane L, et al. CONSORT 2010 statement: extension to
randomised pilot and feasibility trials. BMJ 2016 Oct 24;355:i5239 [FREE Full text] [Medline: 27777223]
25. Sutherland R, Brown A, Nathan N, Janssen L, Reynolds R, Walton A, et al. Protocol for an effectiveness- implementation
hybrid trial to assess the effectiveness and cost-effectiveness of an m-health intervention to decrease the consumption of
discretionary foods packed in school lunchboxes: the 'SWAP IT' trial. BMC Public Health 2019 Nov 12;19(1):1510 [FREE
Full text] [doi: 10.1186/s12889-019-7725-x] [Medline: 31718597]
26. Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for implementation research:
conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health 2011 Mar;38(2):65-76
[FREE Full text] [doi: 10.1007/s10488-010-0319-7] [Medline: 20957426]
27. Wolfenden L, Reilly K, Kingsland M, Grady A, Williams CM, Nathan N, et al. Identifying opportunities to develop the
science of implementation for community-based non-communicable disease prevention: A review of implementation trials.
Prev Med 2019 Jan;118:279-285. [doi: 10.1016/j.ypmed.2018.11.014] [Medline: 30468794]
28. Blom-Hoffman J, Leff SS, Franko DL, Weinstein E, Beakley K, Power TJ. Consent procedures and participation rates in
school-based intervention and prevention research: using a multi-component, partnership-based approach to recruit
participants. School Ment Health 2009 Mar 01;1(1):3-15 [FREE Full text] [doi: 10.1007/s12310-008-9000-7] [Medline:
19834586]
29. Pechey R, Monsivais P. Socioeconomic inequalities in the healthiness of food choices: Exploring the contributions of food
expenditures. Prev Med 2016 Jul;88:203-209 [FREE Full text] [doi: 10.1016/j.ypmed.2016.04.012] [Medline: 27095324]
30. Census of Population and Housing: Socio-Economic Indexes for Australia (SEIFA). Australian Bureau of Statistics (ABS).
Canberra; 2016. URL: https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/2033.0.55.0012016?OpenDocument
[accessed 2021-05-01]
31. Michie S, van SMM, West R. The behaviour change wheel: a new method for characterising and designing behaviour
change interventions. Implement Sci 2011;6:42 [FREE Full text] [doi: 10.1186/1748-5908-6-42] [Medline: 21513547]
32. Reynolds R, Sutherland R, Nathan N, Janssen L, Lecathelinais C, Reilly K, et al. Feasibility and principal acceptability of
school-based mobile communication applications to disseminate healthy lunchbox messages to parents. Health Promot J
Austr 2019 Jan;30(1):108-113. [doi: 10.1002/hpja.57] [Medline: 29719083]
33. Nutrition in schools policy. NSW Department of Education. 2011. URL: https://policies.education.nsw.gov.au/policy-library/
policies/nutrition-in-schools-policy [accessed 2021-05-01]
34. Brown A, Sutherland R, Janssen L, Hudson N, Chooi A, Reynolds R, et al. Enhancing the potential effects of text messages
delivered via an m-health intervention to improve packing of healthy school lunchboxes. Public Health Nutr 2020 Oct
14:1-10. [doi: 10.1017/s1368980020003997]
35. Kremer PJ, Bell AC, Swinburn BA. Calibration and reliability of a school food checklist: a new tool for assessing school
food and beverage consumption. Asia Pac J Clin Nutr 2006;15(4):465-473 [FREE Full text] [Medline: 17077061]
36. Mitchell SA, Miles CL, Brennan L, Matthews J. Reliability of the School Food Checklist for in-school audits and photograph
analysis of children's packed lunches. J Hum Nutr Diet 2010 Feb;23(1):48-53. [doi: 10.1111/j.1365-277X.2009.00996.x]
[Medline: 19788706]
37. Hardy L, Mihrshahi S, Drayton B, Bauman A. NSW schools physical activity and nutrition survey (SPANS) 2015: full
report. NSW Department of Health. Sydney; 2016. URL: https://www.health.nsw.gov.au/heal/Publications/
spans-2015-full-report.PDF [accessed 2021-05-01]
38. Miles CL, Matthews J, Brennan L, Mitchell S. Changes in the content of children's school lunches across the school week.
Health Promot J Austr 2010 Dec;21(3):196-201 [FREE Full text] [doi: 10.1071/he10196] [Medline: 21118066]
39. Nathan N, Wolfenden L, Butler M, Bell AC, Wyse R, Campbell E, et al. Vegetable and fruit breaks in Australian primary
schools: prevalence, attitudes, barriers and implementation strategies. Health Educ Res 2011 Aug;26(4):722-731. [doi:
10.1093/her/cyr033] [Medline: 21571836]
40. Sorhaindo A, Feinstein L. Wider Benefits of Learning Research Report no. 18. What Is the Relationship Between Child
Nutrition and School Outcomes?. London: Centre for Research on the Wider Benefits of Learning; 2006.
41. Fredricks J, Blumenfeld P, Friedel J. School engagement. In: Moore KA, Lippman LH, editors. What Do Children Need
to Flourish? Conceptualising and Measuring Indicators of Positive Development. New York: Springer Science and Business
Media; 2005:305-321.
https://www.jmir.org/2021/6/e25256/ J Med Internet Res 2021 | vol. 23 | iss. 6 | e25256 | p. 15
(page number not for citation purposes)
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